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Big data in 2016 vs 2017 – What’s changed and what’s to come?

Posted by Colin Blair on Feb 14, 2017 12:42:46 PM

Last year, I gave a presentation to industry executives where I discussed my five big data predictions for 2016. I don’t make a lot of predictions personally, but I enjoy understanding the merits of why others would do so. I thought it would be interesting to revisit these thought-provoking predictions and posit some thoughts of my own for 2017.

#1: The rise of the Chief Data Officer

By now we are witnesses to the proliferation of data and its increasing role in innovation and competitive differentiation. What was once manageable, structured and valuable has become unmanageable, unstructured and extremely valuable – 24k gold according to some in the industry. This “gold” has to be secured, transmitted, stored, extracted, cleansed, blended, integrated, analyzed, archived – the list goes on. Mining and refining is hard work and requires strategic consideration.

The formation of the Chief Data Officer role came about as a direct response to the question, “How can we be strategic with our data?” With the explosion of data in the enterprise and its inherent value, organizations need to have a corporate data strategy and - equally as important - someone to manage, own and drive that strategy.

This c-suite position isn’t reserved for just large enterprises – small companies and tech startups alike have CDOs. Adoption has been slower for many Fortune 1000 companies but most are advancing with data initiatives, such as piloting high-performance data teams, but they haven’t necessarily ratified their strategies around data just yet. In 2017, we can expect these pilot programs to increase and to align with business initiatives and outcomes.

Whether or not a company has a data strategy, data exists and persists throughout the enterprise. My prediction for 2017 is that the needs for data-driven decision making will abound as business leaders look to reign in siloed data to create business insight through data analytics. This will further expand and drive data strategies, which will be formally and increasingly led by a Chief Data Officer.

#2: Power to the business users

Data is useless unless you can find a way to harness and leverage it. In the not-so-distant past, data management and administration was done by specialists in IT. Last year we saw a significant increase in the number of business users and the power of the tools available to them. Click-to-download and limited license trials were used to entice business users to try different software solutions and approaches, but all of these technologies, resources and independence drove disparate initiatives. It all comes back to having a strategy. In 2017, organizations will need monetize their data, organize their efforts and prioritize their activities to drive business outcomes.

That’s the best part about big data, analytics and cognitive computing; it’s all about using the value of the data to drive business outcomes. Once a company figures out what it is it wants to achieve, data can be used to optimize how the business is run.

A retail organization may ask, “How can we better sell products to our customers?” A hospital may ask, “How can we improve patient care?” An organization within the public sector may ask, “How can we create better services for our constituents?” In 2017, power will go to the line of business with the help and focus of the business users.

#3: Embedding the intelligence

Rather than storing data, mining it later and reporting it even later, many Independent Software Vendors (ISVs) are embedding the intelligence in the application by streaming data, analyzing in real time, and visualizing it for users immediately.

In today’s era of IT, time to market is a big deal – think about traders in the financial markets or marketers in the Twitterverse. The more intelligence that can embedded, the more information you can extract and the sooner you can make an informed analysis.

Consider Major League Baseball. As a viewer, you’re likely much more interested in the game when you can see in the top right corner of the screen how fast the last pitch was, or how accurately the umpire is calling balls and strikes, or how the batter did in similar pitch counts this season. The more informed your users are (like with MLB fans), the more engaged and satisfied they become.

In 2017, as companies will look to engage more users and consumers, embedding intelligence using big data and analytics will play a key role in customer acceptance which can drive share gains in the marketplace.

#4: Shortage of talent to meet demand of data and analytics solutions

With an increase in demand for big data and analytics solutions, there must be a requisite supply of talented workers to meet that demand. This remains especially true for unstructured data, voluminous data sets, and cognitive analytics. One of the questions in 2016 was, “Where do I find a data scientist and can I afford to keep them?” The bad news is that currently, the talent supply isn’t keeping pace with the demand. Big data can, at times, require specialist skills that combine programming, statistics, business acumen and years of experience. The truth is that there is a dearth of existing analytics professionals in the marketplace. It can take years for people to cycle through a Bachelor or Master of Science degree in analytics.

The good news is that degree plans are popping up at many of our universities and students that began these programs a couple of years ago are starting move into the workplace. Many schools are creating certificate programs aimed at graduating students in two years with a specialization in big data and analytics.

Fortunately, software suppliers continue to make advancements in use case frameworks, automation and sophistication. And not everyone needs a data scientist, at least initially. I have found first hand - through hackathons that we have hosted - that many of the workers coming out of college have very good technical skills but will need direction and business oversight. For people looking for supplier-specific courses, companies like Tech Data provide education, enablement and certifications online as well as in-person.

The talent shortage will continue beyond 2017. If you have a great employee, make sure that you are taking care of them. For short-term data initiatives, look to partner in a data analytics ecosystem.

#5: Machine learning gains momentum

Machine learning has everything to do with the internet of things (IoT), artificial intelligence (AI), and cloud computing. According to Cisco, the number of intelligent and interconnected devices will skyrocket to over 50 billion devices in 2020. These sensors, tags, appliances, motors, machines, mobile devices and computers are all collecting immense amounts of data and there simply aren’t enough human beings on the planet to analyze it all. Machine learning will increase as algorithms make inferences from data and patterns.

Advances in cloud computing pricing, power, availability and performance have set the table for cognitive computing platforms like IBM Watson to show their capabilities. In 2017, decision automation and machine learning will occur in proofs of technology and proofs of concepts primarily in the makers market and industrial sector.

What do you think 2017 holds for big data and analytics? Please post your reply below.

Tags: Big Data, Analytics, Chief Data Officer, Data, Data Analytics, Data Strategy, Big Data and Analytics, Machine Learning